TL;DR
The SHAP-interpretable XGBoost model demonstrates strong discriminative performance in predicting BE risk in RA, highlighting synovial PDI and synovial hyperplasia as pivotal factors.
This single-center study developed an exploratory prediction model that, after optimism correction, showed modest discrimination and may serve as a preliminary screening framework to identify postmenopausal patients at high risk of rapid bone loss related to early AI therapy.
A transparent, externally validated seven-variable model provides individualised DXA-defined osteoporosis risk estimation in pSS and may help clinicians prioritise bone density testing during routine visits.
A risk prediction model based on 16 common variables, including IL-8, is successfully developed, providing critical evidence for the early identification and prevention of osteoporosis patients, with potential clinical significance.
BACKGROUND Knee osteoarthritis (KOA) is a prevalent degenerative joint disease that causes pain, reduced mobility, and an increased risk of falls in older adults. Predicting fall risk in this population remains difficult because of the complex interplay of multiple risk factors. METHODS Data from 1097 patients with K...
Bone mass and bone structure are both diminished during the course of osteoporosis, a degenerative bone disease. For millions of individuals all around the globe, it is the primary source of suffering. In order to intervene and treat osteoporosis in a timely manner, early prognosis of the disease is crucial. The sugges...
An integrated post-preparation model combining patient characteristics and PRP preparation parameters showed favorable performance in predicting short-term response after PRP therapy in patients with osteoarthritic joint disease.
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